Plasma only samples
import pandas as pd
samples = pd.read_csv('data/sample_sheet.csv')
# must be Healthy Control Study and must pass MiSeq QC
samples = samples.loc[(samples['Study'] == 'Healthy Controls') & (samples['MISEQ.QC.PASS'] == 'PASS')]
samples = samples.set_index('MT.Unique.ID').sort_values(by=['Participant.ID', 'Source'])
# capitalize & strip whitespace for consistency
for column in ['Gender', 'Race', 'Source']:
samples[column] = samples[column].str.capitalize()
samples[column] = samples[column].str.strip()
# use correct ontology terms
race_ontology = {'Asian': 'Asian',
'Black or african american': 'African_American',
'Mixed/asian & white': 'Multiracial',
'Mixed/asian &black': 'Multiracial',
'Mixed/black, white, asian': 'Multiracial',
'Native hawiian or other pacific islander': 'Pacific_Islander',
'Pacific islander': 'Pacific_Islander',
'White': 'White'}
for id in samples.index:
race = samples.at[id, 'Race']
samples.at[id, 'Race'] = race_ontology[race] if race in race_ontology else 'Multiracial'
# get only the plasma samples
mir_counts = pd.read_csv("data/get_canonical/canon_mir_counts.csv", index_col=0)
samples.index = pd.Index(['X' + str(row) for row in samples.index])
plasma_samples = samples.loc[samples['Source'] == "Plasma"]
plasma_mir_counts = mir_counts[plasma_samples.index]
plasma_samples.to_csv('data/plasma_samples.csv')
plasma_mir_counts.to_csv('data/plasma_mir_counts.csv')
Load the sample data and miRNA counts
samples <- read.csv('data/plasma_samples.csv')
counts <- read.csv('data/plasma_mir_counts.csv')
samples <- subset(samples, Library.Generation.Set != "SetRecheck" ) # exclude SetRecheck samples
samples <- samples[samples$X != "X11" & samples$X != "X92",] # remove outliers
samples$Participant.ID <- factor(samples$Participant.ID) # ID is categorical, not numerical
rownames(counts) = counts$X
rownames(samples) = samples$X
counts$X <- NULL # remove extra column
samples$X <- NULL
counts <- counts[,unique(rownames(samples))]
head(samples)
head(counts)
Filter
library(edgeR)
design <- model.matrix(~0+Race+Gender+Age, samples)
dge = DGEList(counts = counts, samples = samples)
# require miRNAs to have CPM > 1 in at least 2 samples
countsPerMillion <- edgeR::cpm(dge)
countCheck <- countsPerMillion > 1
head(countCheck)
keep <- which(rowSums(countCheck) >= 2)
dge <- dge[keep,]
Explore variance
library(SingleCellExperiment)
library(scater)
Attaching package: ‘scater’
The following object is masked from ‘package:EDASeq’:
plotRLE
The following object is masked from ‘package:S4Vectors’:
rename
The following object is masked from ‘package:limma’:
plotMDS
The following object is masked from ‘package:stats’:
filter
reads_sce <- SingleCellExperiment(assays=list(counts=dge$counts), colData=dge$samples)
# remove unexpressed miRNAs
keep_feature <- rowSums(counts(reads_sce) > 0) > 0
reads_sce <- reads_sce[keep_feature, ]
reads_sce <- calculateQCMetrics(reads_sce)
Note that the names of some metrics have changed, see 'Renamed metrics' in ?calculateQCMetrics.
Old names are currently maintained for back-compatibility, but may be removed in future releases.
# log transform
cpm(reads_sce) <- calculateCPM(reads_sce)
reads_sce <- normalize(reads_sce)
using library sizes as size factors
logcounts(reads_sce) <- log2(calculateCPM(reads_sce) + 1)
# visualize
hist(reads_sce$total_counts, breaks=100) # counts per sample

hist(reads_sce$total_features, breaks=100) # counts per miRNA

plotQC(reads_sce, type = "highest-expression")

plotQC(reads_sce, type="explanatory-variables", variables=c("Index", "Participant.ID", "Collection.Date", "Library.Generation.Set", "MiSeq.QC.Run", 'total_features', "Age", "Race", "Gender"))

Examine sources of variation
plotPCA(reads_sce, exprs_values = "logcounts", colour_by = "Library.Generation.Set", size_by = "total_features")
non-plotting arguments like 'exprs_values' should go in 'run_args'

plotPCA(reads_sce, exprs_values = "logcounts", colour_by = "Race", shape_by="Gender", size_by = "total_features")
non-plotting arguments like 'exprs_values' should go in 'run_args'

for (var in c("total_features", "Age", "Library.Generation.Set", "Index", "Participant.ID", "Race", "Gender", "Collection.Date")) {
print(
plotQC(reads_sce, type = "find-pcs", exprs_values = "logcounts", variable = var)
) + ggtitle(var)
}








Normalize & Remove unwanted sources of variation
library(RUVSeq)
library(ggplot2)
library(mvoutlier)
Loading required package: sgeostat
sROC 0.1-2 loaded
dge <- calcNormFactors(dge)
dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)
fit <- glmFit(dge, design)
res <- residuals(fit, type="deviance")
set <- newSeqExpressionSet(dge$counts, phenoData=dge$samples)
ruvr_sets <- list()
for(k in 1:5) {
ruvr_sets[[k]] <- RUVr(set, row.names(dge), k=k, res)
assay(reads_sce, paste("RUVr k=", toString(k))) <- log2(t(t(assayData(ruvr_sets[[k]])$normalizedCounts) / colSums(assayData(ruvr_sets[[k]])$normalizedCounts) * 1e6) + 1)
}
for(n in assayNames(reads_sce)) {
print(
plotPCA(
reads_sce,
colour_by = "Library.Generation.Set",
size_by = "total_features",
exprs_values = n
) + ggtitle(paste("Batch",n))
)
print(
plotPCA(
reads_sce,
colour_by = "Race",
shape_by = "Gender",
size_by = "total_features",
exprs_values = n
) + ggtitle(paste("Demographics", n))
)
}
non-plotting arguments like 'exprs_values' should go in 'run_args'
















Detect outliers
reads_sce <- runPCA(reads_sce, use_coldata = TRUE, detect_outliers = TRUE)
failed to find 'pct_counts_feature_control' in column metadatafailed to find 'total_features_by_counts_feature_control' in column metadatafailed to find 'log10_total_counts_endogenous' in column metadatafailed to find 'log10_total_counts_feature_control' in column metadata
outliers <- colnames(reads_sce)[reads_sce$outlier]
head(outliers)
character(0)
Examine sources of variance after removing unwanted variation
for (var in c("total_features", "Age", "Library.Generation.Set", "Index", "Participant.ID", "Race", "Gender", "Collection.Date")) {
print(
plotQC(reads_sce, type = "find-pcs", exprs_values = "RUVr k= 2", variable = var)
) + ggtitle(var)
}








Visualize top highly-expressed miRNAs male-vs-female


Visualize top highly-expressed miRNAs by Race
# Rank the mean expression values for plasma/serum miRs. Highest expression = 1
mean.expr.afr.rank <- rank(-1*rowMeans(norm.expr.matr[, colData(reads_sce)$Race=="African_American"]))
mean.expr.asn.rank <- rank(-1*rowMeans(norm.expr.matr[, colData(reads_sce)$Race=="Asian"]))
mean.expr.wht.rank <- rank(-1*rowMeans(norm.expr.matr[, colData(reads_sce)$Race=="White"]))
top_N <- 20
top.miRs <- row.names(norm.expr.matr)[mean.expr.afr.rank <= top_N | mean.expr.asn.rank <= top_N | mean.expr.wht.rank <= top_N] # get the names of the top miRs in plasma or serum
norm.expr.top <- norm.expr.matr[top.miRs, ] # Get the expression matrix for the top mIRs
norm.expr.melt <- reshape2::melt(norm.expr.top) # Convert your normalized expression matrix to a 3 column data.frame (row.name, col.name, expression value). Melt is in the dpylr package, I believe.
colnames(norm.expr.melt) <- c("miR.ID", "MT.Unique.ID", "norm.expr") # just so it's easier for me to tell you which columns I'm using.
norm.expr.melt$Race <- ""
for (row_num in 1:nrow(norm.expr.melt)){ # Pull the Gender values from the column metadata.
mt_unique_id <- norm.expr.melt[row_num, ]$MT.Unique.ID
norm.expr.melt[row_num, "Race"] <- as.character(samples[mt_unique_id,"Race"])
}
norm.expr.melt <- norm.expr.melt[norm.expr.melt$Race %in% c("African_American", "Asian", "White"),]
# This would be a simple boxplot with plasma/serum side-by-side. Overlaying the boxes over points takes a little more tweaking to get the dodge/width right, but it's doable.
ggplot(norm.expr.melt, aes(x=reorder(miR.ID, norm.expr, FUN="median"), y=norm.expr, fill=Race)) + geom_boxplot(pos="dodge", outlier.size=0.5) +
ggtitle("Top 20 Expressed miRNAs") + ylab("Normalized Expression") + xlab("miRNA ID") +
theme(panel.grid.major.x = element_line(size = 0.5, linetype = 'solid', colour = "grey"),
panel.grid.major.y = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1))

ggplot(norm.expr.melt, aes(x=reorder(miR.ID, norm.expr, FUN="median"), y=norm.expr, fill=Race)) + geom_boxplot(pos="dodge", outlier.size=0.5) +
ggtitle("Top 20 Expressed miRNAs") + ylab("Normalized Expression") + xlab("miRNA ID") +
theme(panel.grid.major.y = element_line(size = 0.5, linetype = 'solid', colour = "grey"),
panel.grid.major.x = element_blank()) + coord_flip()

Heatmap of most highly expressed miRNAs
library(pheatmap)
ruvr2 <- ruvr_sets[[2]]
norm.expr.matr <- norm.expr.matr[order(rowMeans(norm.expr.matr), decreasing=TRUE),]
Demographics <- pData(ruvr2)[,c("Age","Race", "Gender")]
annotations <- as.data.frame(Demographics)
rownames(annotations) <- rownames(pData(ruvr2))
pheatmap(norm.expr.matr[0:50,], cluster_rows=FALSE, show_rownames=TRUE, cluster_cols=TRUE, annotation_col=annotations, main="Top 50 Expressed miRNAs")

DE analysis with EdgeR
# based on Ryan's code, pass RUVSeq-corrected data to edgeR
ruvr2 <- ruvr_sets[[2]]
design <- model.matrix(~ 0 + Race + Gender + Age + W_1 + W_2, pData(ruvr2))
dge <- estimateDisp(dge, design = design, tagwise = TRUE, robust = TRUE)
fit <- glmFit(dge, design)
Core miRNAs stable across individuals (Fig2D)
ruvr_norm_matrix <- assayData(ruvr2)$normalizedCounts
#ruvr_norm_matrix <- counts
median <- apply(ruvr_norm_matrix, 1, median)
median_expr_df <- as.data.frame(median)
median_expr_df$qcd <- apply(ruvr_norm_matrix, 1, FUN=function(x){ q <- quantile(x, c(0.25, 0.75)); diff(q)/sum(q) }) # quartile coeff. of dispersion
median_expr_df$cv <- apply(ruvr_norm_matrix, 1, FUN=function(x){ 100*sd(x)/mean(x) }) # coefficient of variation
# log-2 transform the medians
median_expr_df$log2_median <- log(median_expr_df$median, 2)
# plot all miRNAs
ggplot(median_expr_df, aes(x=qcd, y=log2_median)) + geom_point() + ylab('Log2 Median Expression') + xlab('Quartile Coefficient of Dispersion') + ggtitle("miRNA QCD in Plasma")

ggplot(median_expr_df, aes(x=cv, y=log2_median)) + geom_point() + ylab('Log2 Median Expression') + xlab('Coefficient of Variation') + ggtitle("miRNA %CV in Plasma")

# plot only miRNAs expressed in 90% of samples
median_expr_df$sample_fraction = (rowSums(ruvr_norm_matrix > 0) / length(rownames(samples)))
sample_fraction_threshold = 0.9
median_expr_df_filt = median_expr_df[median_expr_df$sample_fraction >= sample_fraction_threshold, ]
ggplot(median_expr_df_filt, aes(x=qcd, y=log2_median)) + geom_point() + ylab('Log2 Median Expression') + xlab('Quartile Coefficient of Dispersion') + ggtitle("miRNA QCD in Plasma")

ggplot(median_expr_df_filt, aes(x=cv, y=log2_median)) + geom_point() + ylab('Log2 Median Expression') + xlab('Coefficient of Variation') + ggtitle("miRNA %CV in Plasma")

Contrast gender (Fig3B)
lrt_gender <- glmLRT(fit, coef = "GenderMale")
alpha_level <- 0.05
results_gender <- data.frame(topTags(lrt_gender, n=Inf, sort.by="PValue", p.value=alpha_level))
sig_miRs_gender = list()
logFC_threshold <- 1
sig_miRs_gender[[1]] <- rownames(results_gender[results_gender$PValue < alpha_level & results_gender$FDR < alpha_level & abs(results_gender$logFC) > logFC_threshold,]) # filter by p-value and logFC
sig_miRs_gender[[2]] <- rownames(results_gender[results_gender$PValue < alpha_level & results_gender$FDR < alpha_level,])
# heatmaps of significant DE miRNAs
Gender <- pData(ruvr2)[,c("Gender")]
annotations_gender <- as.data.frame(Gender)
rownames(annotations_gender) <- rownames(pData(ruvr2))
for(miR_list in sig_miRs_gender) {
pheatmap(norm.expr.matr[miR_list,], cluster_rows=TRUE, show_rownames=TRUE, cluster_cols=TRUE, annotation_col=annotations_gender, main="Differentially Expressed miRNAs")
}


# TODO: boxplots in one figure
sig_miRs_gender_table <- norm.expr.matr[sig_miRs_gender[[1]],]
sig_miRs_gender_table <- melt(sig_miRs_gender_table)
colnames(sig_miRs_gender_table) <- c("miRNA", "Sample", "Expression")
annotations_gender$Sample <- rownames(annotations_gender)
sig_miRs_gender_table <- merge(sig_miRs_gender_table, annotations_gender, by="Sample")
ggplot(data = sig_miRs_gender_table, aes(x=miRNA, y=Expression)) + geom_boxplot(aes(fill=Gender)) +
facet_wrap(~ miRNA, scales="free") +
ggtitle("Differentially Expressed miRNAs in Plasma by Gender")

Contrast race (Fig3B)
lrt_race <- glmLRT(fit, contrast=makeContrasts(asn=(RaceAsian-(RaceAfrican_American+RaceWhite)/2),
afr=(RaceAfrican_American-(RaceAsian+RaceWhite)/2),
wht=(RaceWhite-(RaceAfrican_American+RaceAsian)/2), levels=design))
results_race <- data.frame(topTags(lrt_race, n=Inf, sort.by="PValue", p.value=alpha_level))
sig_miRs_race_list = list()
sig_miRs_race_list[[1]] <- rownames(results_race[results_race$PValue < alpha_level & results_race$FDR < alpha_level & abs(results_race$logFC.asn) > logFC_threshold | abs(results_race$logFC.afr) > logFC_threshold | abs(results_race$logFC.wht) > logFC_threshold,]) # filter by p-value and logFC
sig_miRs_race_list[[2]] <- rownames(results_race[results_race$PValue < alpha_level & results_race$FDR < alpha_level,])
# heatmaps of significant DE miRNAs
Race <- pData(ruvr2)[,c("Race")]
annotations_race <- as.data.frame(Race)
rownames(annotations_race) <- rownames(pData(ruvr2))
for(miR_list in sig_miRs_race_list) {
pheatmap(norm.expr.matr[miR_list,], cluster_rows=TRUE, show_rownames=TRUE, cluster_cols=TRUE, annotation_col=annotations_race, main="Differentially Expressed miRNAs")
}


# boxplots of significant DE miRNAs
sig_miRs_race_table <- norm.expr.matr[sig_miRs_race_list[[1]],]
sig_miRs_race_table <- melt(sig_miRs_race_table)
colnames(sig_miRs_race_table) <- c("miRNA", "Sample", "Expression")
annotations_race$Sample <- rownames(annotations_race)
sig_miRs_race_table <- merge(sig_miRs_race_table, annotations_race, by="Sample")
sig_miRs_race_table <- sig_miRs_race_table[sig_miRs_race_table$Race != "Multiracial" & sig_miRs_race_table$Race != "Pacific_Islander",] # Pacific_Islander and Multiracial have too small sample sizes
ggplot(data = sig_miRs_race_table, aes(x=miRNA, y=Expression)) + geom_boxplot(aes(fill=Race)) +
facet_wrap(~ miRNA, scales="free") +
ggtitle("Differentially Expressed miRNAs in Plasma by Race")

Create a PCA plot showing Age x Gender
plotPCA(
reads_sce,
colour_by = "Age",
shape_by = "Gender",
size_by = "total_features",
exprs_values = "RUVr k= 2"
) + ggtitle("RUVSeq-Normalized Expression (k=2)")
non-plotting arguments like 'exprs_values' should go in 'run_args'

---
title: "miRNA Differential expression analysis for plasma samples"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---
# Plasma only samples
```{python3}
import pandas as pd
samples = pd.read_csv('data/sample_sheet.csv')
# must be Healthy Control Study and must pass MiSeq QC
samples = samples.loc[(samples['Study'] == 'Healthy Controls') & (samples['MISEQ.QC.PASS'] == 'PASS')]  
samples = samples.set_index('MT.Unique.ID').sort_values(by=['Participant.ID', 'Source'])
# capitalize & strip whitespace for consistency
for column in ['Gender', 'Race', 'Source']:
    samples[column] = samples[column].str.capitalize()
    samples[column] = samples[column].str.strip()
# use correct ontology terms
race_ontology = {'Asian': 'Asian',
 'Black or african american': 'African_American',
 'Mixed/asian & white': 'Multiracial',
 'Mixed/asian &black': 'Multiracial',
 'Mixed/black, white, asian': 'Multiracial',
 'Native hawiian or other pacific islander': 'Pacific_Islander',
 'Pacific islander': 'Pacific_Islander',
 'White': 'White'}
for id in samples.index:
    race = samples.at[id, 'Race']
    samples.at[id, 'Race'] = race_ontology[race] if race in race_ontology else 'Multiracial'
# get only the plasma samples
mir_counts = pd.read_csv("data/get_canonical/canon_mir_counts.csv", index_col=0)
samples.index = pd.Index(['X' + str(row) for row in samples.index])
plasma_samples = samples.loc[samples['Source'] == "Plasma"]
plasma_mir_counts = mir_counts[plasma_samples.index]
plasma_samples.to_csv('data/plasma_samples.csv')
plasma_mir_counts.to_csv('data/plasma_mir_counts.csv')
```

## Load the sample data and miRNA counts
```{r}
samples <- read.csv('data/plasma_samples.csv')
counts <- read.csv('data/plasma_mir_counts.csv')
samples <- subset(samples, Library.Generation.Set != "SetRecheck" )  # exclude SetRecheck samples
samples <- samples[samples$X != "X11" & samples$X != "X92",] # remove outliers
samples$Participant.ID <- factor(samples$Participant.ID)  # ID is categorical, not numerical
rownames(counts) = counts$X
rownames(samples) = samples$X
counts$X <- NULL  # remove extra column
samples$X <- NULL
counts <- counts[,unique(rownames(samples))]
head(samples)
head(counts)
```

## Filter
```{r}
library(edgeR)
design <- model.matrix(~0+Race+Gender+Age, samples)
dge = DGEList(counts = counts, samples = samples)
# require miRNAs to have CPM > 1 in at least 2 samples
countsPerMillion <- edgeR::cpm(dge)
countCheck <- countsPerMillion > 1
head(countCheck)
keep <- which(rowSums(countCheck) >= 2) 
dge <- dge[keep,]
```
## Explore variance
```{r}
library(SingleCellExperiment)
library(scater)
reads_sce <- SingleCellExperiment(assays=list(counts=dge$counts),  colData=dge$samples)
# remove unexpressed miRNAs
keep_feature <- rowSums(counts(reads_sce) > 0) > 0
reads_sce <- reads_sce[keep_feature, ]
reads_sce <- calculateQCMetrics(reads_sce)
# log transform
cpm(reads_sce) <- calculateCPM(reads_sce)
reads_sce <- normalize(reads_sce)
logcounts(reads_sce) <- log2(calculateCPM(reads_sce) + 1)
# visualize
hist(reads_sce$total_counts, breaks=100)  # counts per sample
hist(reads_sce$total_features, breaks=100)  # counts per miRNA
plotQC(reads_sce, type = "highest-expression")
plotQC(reads_sce, type="explanatory-variables", variables=c("Index", "Participant.ID", "Collection.Date", "Library.Generation.Set", "MiSeq.QC.Run", 'total_features', "Age", "Race", "Gender"))
```
## Examine sources of variation
```{r}
plotPCA(reads_sce, exprs_values = "logcounts", colour_by = "Library.Generation.Set", size_by = "total_features")
plotPCA(reads_sce, exprs_values = "logcounts", colour_by = "Race", shape_by="Gender", size_by = "total_features")
for (var in c("total_features", "Age", "Library.Generation.Set", "Index", "Participant.ID", "Race", "Gender", "Collection.Date")) {
  print(
    plotQC(reads_sce, type = "find-pcs", exprs_values = "logcounts", variable = var)
    )  + ggtitle(var) 
  }
```

## Normalize & Remove unwanted sources of variation
```{r}
library(RUVSeq)
library(ggplot2)
library(mvoutlier)
dge <- calcNormFactors(dge)
dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)
fit <- glmFit(dge, design)
res <- residuals(fit, type="deviance")
set <- newSeqExpressionSet(dge$counts, phenoData=dge$samples)
ruvr_sets <- list()
for(k in 1:5) {
  ruvr_sets[[k]] <- RUVr(set, row.names(dge), k=k, res)
  assay(reads_sce, paste("RUVr k=", toString(k))) <- log2(t(t(assayData(ruvr_sets[[k]])$normalizedCounts) / colSums(assayData(ruvr_sets[[k]])$normalizedCounts) * 1e6) + 1)
}
for(n in assayNames(reads_sce)) {
  print(
        plotPCA(
            reads_sce,
            colour_by = "Library.Generation.Set",
            size_by = "total_features",
            exprs_values = n
        ) + ggtitle(paste("Batch",n))
  )
  print(
        plotPCA(
            reads_sce,
            colour_by = "Race",
            shape_by = "Gender",
            size_by = "total_features",
            exprs_values = n
        ) + ggtitle(paste("Demographics", n))        
  )
}
```

## Detect outliers
```{r}
reads_sce <- runPCA(reads_sce, use_coldata = TRUE, detect_outliers = TRUE)
outliers <- colnames(reads_sce)[reads_sce$outlier]
head(outliers)
```

## Examine sources of variance after removing unwanted variation
```{r}
for (var in c("total_features", "Age", "Library.Generation.Set", "Index", "Participant.ID", "Race", "Gender", "Collection.Date")) {
  print(
    plotQC(reads_sce, type = "find-pcs", exprs_values = "RUVr k= 2", variable = var)
    )  + ggtitle(var) 
}
```
## Visualize top highly-expressed miRNAs male-vs-female
```{r}
library(RColorBrewer)
library(reshape2)
# Ryan's code, modified
norm.expr.matr <- exprs(reads_sce)
# Rank the mean expression values for plasma/serum miRs. Highest expression = 1
mean.expr.male.rank <- rank(-1*rowMeans(norm.expr.matr[, colData(reads_sce)$Gender=="Male"]))
mean.expr.female.rank <- rank(-1*rowMeans(norm.expr.matr[, colData(reads_sce)$Gender=="Female"]))
top_N <- 20
top.miRs <- row.names(norm.expr.matr)[mean.expr.male.rank <= top_N | mean.expr.female.rank <= top_N] # get the names of the top miRs in plasma or serum
norm.expr.top <- norm.expr.matr[top.miRs, ] # Get the expression matrix for the top mIRs
norm.expr.melt <- reshape2::melt(norm.expr.top) # Convert your normalized expression matrix to a 3 column data.frame (row.name, col.name, expression value). Melt is in the dpylr package, I believe.
colnames(norm.expr.melt) <- c("miR.ID", "MT.Unique.ID", "norm.expr") # just so it's easier for me to tell you which columns I'm using.
norm.expr.melt$Gender <- ""
for (row_num in 1:nrow(norm.expr.melt)){  # Pull the Gender values from the column metadata.
  mt_unique_id <- norm.expr.melt[row_num, ]$MT.Unique.ID
  norm.expr.melt[row_num, "Gender"] <- as.character(samples[mt_unique_id,"Gender"])
}
# This would be a simple boxplot with plasma/serum side-by-side. Overlaying the boxes over points takes a little more tweaking to get the dodge/width right, but it's doable.
ggplot(norm.expr.melt, aes(x=reorder(miR.ID, norm.expr, FUN = "median"), y=norm.expr, fill=Gender)) + geom_boxplot(pos="dodge", outlier.size=0.5) + 
  ggtitle("Top 20 Expressed miRNAs") + ylab("Normalized Expression") + xlab("miRNA ID") + 
  theme(panel.grid.major.x = element_line(size = 0.5, linetype = 'solid', colour = "grey"), 
        panel.grid.major.y = element_blank(),
        axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(norm.expr.melt, aes(x=reorder(miR.ID, norm.expr, FUN="median"), y=norm.expr, fill=Gender)) + geom_boxplot(pos="dodge", outlier.size=0.5) + 
  ggtitle("Top 20 Expressed miRNAs") + ylab("Normalized Expression") + xlab("miRNA ID") + 
  theme(panel.grid.major.y = element_line(size = 0.5, linetype = 'solid', colour = "grey"), 
        panel.grid.major.x = element_blank()) + coord_flip()
```
## Visualize top highly-expressed miRNAs by Race
```{r}
# Rank the mean expression values for plasma/serum miRs. Highest expression = 1
mean.expr.afr.rank <- rank(-1*rowMeans(norm.expr.matr[, colData(reads_sce)$Race=="African_American"]))
mean.expr.asn.rank <- rank(-1*rowMeans(norm.expr.matr[, colData(reads_sce)$Race=="Asian"]))
mean.expr.wht.rank <- rank(-1*rowMeans(norm.expr.matr[, colData(reads_sce)$Race=="White"]))
top_N <- 20
top.miRs <- row.names(norm.expr.matr)[mean.expr.afr.rank <= top_N | mean.expr.asn.rank <= top_N | mean.expr.wht.rank <= top_N] # get the names of the top miRs in plasma or serum
norm.expr.top <- norm.expr.matr[top.miRs, ] # Get the expression matrix for the top mIRs
norm.expr.melt <- reshape2::melt(norm.expr.top) # Convert your normalized expression matrix to a 3 column data.frame (row.name, col.name, expression value). Melt is in the dpylr package, I believe.
colnames(norm.expr.melt) <- c("miR.ID", "MT.Unique.ID", "norm.expr") # just so it's easier for me to tell you which columns I'm using.
norm.expr.melt$Race <- ""
for (row_num in 1:nrow(norm.expr.melt)){  # Pull the Gender values from the column metadata.
  mt_unique_id <- norm.expr.melt[row_num, ]$MT.Unique.ID
  norm.expr.melt[row_num, "Race"] <- as.character(samples[mt_unique_id,"Race"])
}
norm.expr.melt <- norm.expr.melt[norm.expr.melt$Race %in% c("African_American", "Asian", "White"),]
# This would be a simple boxplot with plasma/serum side-by-side. Overlaying the boxes over points takes a little more tweaking to get the dodge/width right, but it's doable.
ggplot(norm.expr.melt, aes(x=reorder(miR.ID, norm.expr, FUN="median"), y=norm.expr, fill=Race)) + geom_boxplot(pos="dodge", outlier.size=0.5) + 
  ggtitle("Top 20 Expressed miRNAs") + ylab("Normalized Expression") + xlab("miRNA ID") + 
  theme(panel.grid.major.x = element_line(size = 0.5, linetype = 'solid', colour = "grey"), 
        panel.grid.major.y = element_blank(),
        axis.text.x = element_text(angle = 90, hjust = 1))
ggplot(norm.expr.melt, aes(x=reorder(miR.ID, norm.expr, FUN="median"), y=norm.expr, fill=Race)) + geom_boxplot(pos="dodge", outlier.size=0.5) + 
  ggtitle("Top 20 Expressed miRNAs") + ylab("Normalized Expression") + xlab("miRNA ID") + 
  theme(panel.grid.major.y = element_line(size = 0.5, linetype = 'solid', colour = "grey"), 
        panel.grid.major.x = element_blank()) + coord_flip()
```

## Heatmap of most highly expressed miRNAs
```{r}
library(pheatmap)
ruvr2 <- ruvr_sets[[2]]
norm.expr.matr <- norm.expr.matr[order(rowMeans(norm.expr.matr), decreasing=TRUE),]
Demographics <- pData(ruvr2)[,c("Age","Race", "Gender")]
annotations <- as.data.frame(Demographics)
rownames(annotations) <- rownames(pData(ruvr2))
pheatmap(norm.expr.matr[0:50,], cluster_rows=FALSE, show_rownames=TRUE, cluster_cols=TRUE, annotation_col=annotations, main="Top 50 Expressed miRNAs")
```

# DE analysis with EdgeR
```{r}
# based on Ryan's code, pass RUVSeq-corrected data to edgeR
ruvr2 <- ruvr_sets[[2]]
design <- model.matrix(~ 0 + Race + Gender + Age + W_1 + W_2, pData(ruvr2))
dge <- estimateDisp(dge, design = design, tagwise = TRUE, robust = TRUE)
fit <- glmFit(dge, design)
```

## Core miRNAs stable across individuals (Fig2D)
```{r}
ruvr_norm_matrix <- assayData(ruvr2)$normalizedCounts
#ruvr_norm_matrix <- counts
median <- apply(ruvr_norm_matrix, 1, median)
median_expr_df <- as.data.frame(median)
median_expr_df$qcd <- apply(ruvr_norm_matrix, 1, FUN=function(x){ q <- quantile(x, c(0.25, 0.75)); diff(q)/sum(q) })  # quartile coeff. of dispersion
median_expr_df$cv <- apply(ruvr_norm_matrix, 1, FUN=function(x){ 100*sd(x)/mean(x) })  # coefficient of variation
# log-2 transform the medians
median_expr_df$log2_median <- log(median_expr_df$median, 2)
# plot all miRNAs
ggplot(median_expr_df, aes(x=qcd, y=log2_median)) + geom_point() + ylab('Log2 Median Expression') + xlab('Quartile Coefficient of Dispersion') + ggtitle("miRNA QCD in Plasma")
ggplot(median_expr_df, aes(x=cv, y=log2_median)) + geom_point() + ylab('Log2 Median Expression') + xlab('Coefficient of Variation') + ggtitle("miRNA %CV in Plasma")
# plot only miRNAs expressed in 90% of samples
median_expr_df$sample_fraction = (rowSums(ruvr_norm_matrix > 0) / length(rownames(samples)))
sample_fraction_threshold = 0.9
median_expr_df_filt = median_expr_df[median_expr_df$sample_fraction >= sample_fraction_threshold, ]
ggplot(median_expr_df_filt, aes(x=qcd, y=log2_median)) + geom_point() + ylab('Log2 Median Expression') + xlab('Quartile Coefficient of Dispersion') + ggtitle("miRNA QCD in Plasma")
ggplot(median_expr_df_filt, aes(x=cv, y=log2_median)) + geom_point() + ylab('Log2 Median Expression') + xlab('Coefficient of Variation') + ggtitle("miRNA %CV in Plasma")
```

## Contrast gender (Fig3B)
```{r}
lrt_gender <- glmLRT(fit, coef = "GenderMale")
alpha_level <- 0.05
results_gender <- data.frame(topTags(lrt_gender, n=Inf, sort.by="PValue", p.value=alpha_level))
sig_miRs_gender_list = list()
logFC_threshold <- 1
sig_miRs_gender_list[[1]] <- rownames(results_gender[results_gender$PValue < alpha_level & results_gender$FDR < alpha_level & abs(results_gender$logFC) > logFC_threshold,])  # filter by p-value and logFC
sig_miRs_gender_list[[2]] <- rownames(results_gender[results_gender$PValue < alpha_level & results_gender$FDR < alpha_level,])
# heatmaps of significant DE miRNAs
Gender <- pData(ruvr2)[,c("Gender")]
annotations_gender <- as.data.frame(Gender)
rownames(annotations_gender) <- rownames(pData(ruvr2))
for(miR_list in sig_miRs_gender_list) {
  pheatmap(norm.expr.matr[miR_list,], cluster_rows=TRUE, show_rownames=TRUE, cluster_cols=TRUE, annotation_col=annotations_gender, main="Differentially Expressed miRNAs")
}
# boxplots of significant DE miRNAs
sig_miRs_gender_table <- norm.expr.matr[sig_miRs_gender_list[[1]],]
sig_miRs_gender_table <- melt(sig_miRs_gender_table)
colnames(sig_miRs_gender_table) <- c("miRNA", "Sample", "Expression")
annotations_gender$Sample <- rownames(annotations_gender)
sig_miRs_gender_table <- merge(sig_miRs_gender_table, annotations_gender, by="Sample")
ggplot(data = sig_miRs_gender_table, aes(x=miRNA, y=Expression)) + geom_boxplot(aes(fill=Gender)) + 
  facet_wrap(~ miRNA, scales="free") + 
  ggtitle("Differentially Expressed miRNAs in Plasma by Gender")
```

## Contrast race (Fig3B)
```{r}
lrt_race <- glmLRT(fit, contrast=makeContrasts(asn=(RaceAsian-(RaceAfrican_American+RaceWhite)/2),
                                          afr=(RaceAfrican_American-(RaceAsian+RaceWhite)/2),
                                          wht=(RaceWhite-(RaceAfrican_American+RaceAsian)/2), levels=design))
results_race <- data.frame(topTags(lrt_race, n=Inf, sort.by="PValue", p.value=alpha_level))
sig_miRs_race_list = list()
sig_miRs_race_list[[1]] <- rownames(results_race[results_race$PValue < alpha_level & results_race$FDR < alpha_level & abs(results_race$logFC.asn) > logFC_threshold | abs(results_race$logFC.afr) > logFC_threshold | abs(results_race$logFC.wht) > logFC_threshold,])  # filter by p-value and logFC
sig_miRs_race_list[[2]] <- rownames(results_race[results_race$PValue < alpha_level & results_race$FDR < alpha_level,])
# heatmaps of significant DE miRNAs
Race <- pData(ruvr2)[,c("Race")]
annotations_race <- as.data.frame(Race)
rownames(annotations_race) <- rownames(pData(ruvr2))
for(miR_list in sig_miRs_race_list) {
  pheatmap(norm.expr.matr[miR_list,], cluster_rows=TRUE, show_rownames=TRUE, cluster_cols=TRUE, annotation_col=annotations_race, main="Differentially Expressed miRNAs")
}
# boxplots of significant DE miRNAs
sig_miRs_race_table <- norm.expr.matr[sig_miRs_race_list[[1]],]
sig_miRs_race_table <- melt(sig_miRs_race_table)
colnames(sig_miRs_race_table) <- c("miRNA", "Sample", "Expression")
annotations_race$Sample <- rownames(annotations_race)
sig_miRs_race_table <- merge(sig_miRs_race_table, annotations_race, by="Sample")
sig_miRs_race_table <- sig_miRs_race_table[sig_miRs_race_table$Race != "Multiracial" & sig_miRs_race_table$Race != "Pacific_Islander",] # Pacific_Islander and Multiracial have too small sample sizes
ggplot(data = sig_miRs_race_table, aes(x=miRNA, y=Expression)) + geom_boxplot(aes(fill=Race)) + 
  facet_wrap(~ miRNA, scales="free") + 
  ggtitle("Differentially Expressed miRNAs in Plasma by Race")
```

## Create a PCA plot showing Age x Gender
```{r}
plotPCA(
            reads_sce,
            colour_by = "Age",
            shape_by = "Gender",
            size_by = "total_features",
            exprs_values = "RUVr k= 2"
) + ggtitle("RUVSeq-Normalized Expression (k=2)") 
```

```{r}
save.image("diff_expr_demographics_plasma_only.RData")
```